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Participatory Action Research and Photovoice: Applicability, Relevance, and Process in Nursing Education Research

2020· article· en· W3040972805 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNursing Education Perspectives · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPhotovoiceParticipatory action researchRelevance (law)Action researchCitizen journalismQualitative researchMedical educationNursingData collectionProcess (computing)Nurse educationAction (physics)PsychologyNursing researchParticipant observationSociologyPedagogyMedicineComputer scienceSocial sciencePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT: Participatory action research (PAR) is a philosophy and approach to qualitative research. The purpose of this article is to generate a clearer understanding of PAR and its relevance to the discipline and profession of nursing. The authors provide a description of the principles and process of implementing PAR methodology, using photovoice as an innovative, participant-directed data collection method in rural nursing preceptorship. Participants were undergraduate nursing students and faculty advisors assigned to rural communities during the final clinical preceptorship. Participants described opportunities and challenges experienced during the preceptorship and how these experiences influenced their learning and overall preceptorship experience.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.770
GPT teacher head0.752
Teacher spread0.018 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it